Transfer learning (TL) is a method used in RL as one way to reduce an agent’s training time [1]}.
The key idea is that an agent can learn a target task faster by transferring information from a previously learned source task, similar to how humans can learn algebra more quickly by transferring knowledge from previous tasks that require addition and multiplication.
Although the concept of TL is intuitively appealing, its effectiveness has been mostly shown through empirical studies [2]}.
As such, we aim to provide new theoretical results for one TL method in RL, \(Q\) -function reuse.
